On the detection and recognition of television commercials
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Accurate repeat finding and object skipping using fingerprints
Proceedings of the 13th annual ACM international conference on Multimedia
Perspectives on the Contribution of Timbre to Musical Structure
Computer Music Journal
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A nature inspired Ying-Yang approach for intelligent decision support in bank solvency analysis
Expert Systems with Applications: An International Journal
Fuzzy CMAC with incremental Bayesian Ying-Yang learning and dynamic rule construction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Audio, Speech, and Language Processing
Instructional Video Content Analysis Using Audio Information
IEEE Transactions on Audio, Speech, and Language Processing
A quick search method for audio and video signals based on histogram pruning
IEEE Transactions on Multimedia
ARGOS: automatically extracting repeating objects from multimedia streams
IEEE Transactions on Multimedia
Efficient Short Video Repeat Identification With Application to News Video Structure Analysis
IEEE Transactions on Multimedia
FCMAC-BYY: Fuzzy CMAC Using Bayesian Ying–Yang Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Nowadays, audio podcasting has been widely used by many online sites such as newspapers, web portals, journals, and so forth, to deliver audio content to users through download or subscription. Within 1 to 30 minutes long of one podcast story, it is often that multiple audio advertisements (ads) are inserted into and repeated, with each of a length of 5 to 30 seconds, at different locations. Automatic detection of these attached ads is a challenging task due to the complexity of the search algorithms. Based on the knowledge of typical structures of podcast contents, this paper proposes a novel efficient advertisement discovery approach for large audio podcasting collections. The proposed approach offers a significant improvement on search speed with sufficient accuracy. The key to the acceleration comes from the advantages of candidate segmentation and sampling technique introduced to reduce both search areas and number of matching frames. The approach has been tested over a variety of podcast contents collected from MIT Technology Review, Scientific American, and Singapore Podcast websites. Experimental results show that the proposed algorithm archives detection rate of 97.5% with a significant computation saving as compared to existing state-of-the-art methods.